In the last few years, many deep learning architectures have been proposed for object recognition. Those deep learning solutions generally achieved better performance, compared with traditional non-deep learning techniques. The Ensemble of Deep Networks (eDN) [1]} represented an early architecture that automatically learns the representations for saliency prediction, blending feature maps from different layers. DeepGaze [2]} fed the responses of different layers of AlexNet [3]} and a predefined center bias into a softmax layer, and generated a probability distribution of human eye fixation. DeepGaze II [4]} (from DeepGaze [2]}), recently was proposed that provides a deeper network with VGG19 [6]}, where the attention information is directly inferred from the original VGGNet, without fine-tuning on attention dataset. Kruthiventi et al. [7]} also proposed a DeepFix model based on VGG16. In [8]}, saliency prediction and object detection were achieved in a deep CNN. SALICON net [9]} captured multi-scale saliency via concatenating fine and coarse features from two stream convolutional networks trained with multi-scale inputs.
In [10]}, two models with shallow and deep network architectures were exploited for saliency prediction. Jetley et al. [11]} tested several loss functions based on probability distance measures, such as \(\chi ^2\)  divergence, total variation distance, cosine distance, KL divergence and Bhattacharyya distance by considering saliency models as generalized Bernoulli distributions.
